<ul data-eligibleForWebStory="false"><li>Researchers introduce SynBridge, a bidirectional flow-based generative model for multi-task reaction prediction, focusing on discrete and abrupt changes in chemical reactions like electron transfer and bond formation.</li><li>SynBridge utilizes a graph-to-graph transformer network architecture with discrete flow bridges to capture bidirectional chemical transformations between reactants and products, emphasizing discrete states of bonds and atoms.</li><li>The proposed method achieves state-of-the-art performance in forward and retrosynthesis tasks on benchmark datasets (USPTO-50K, USPTO-MIT, Pistachio), showcasing its effectiveness.</li><li>Experiments, ablation studies, and noise scheduling analysis highlight the advantages of structured diffusion over discrete spaces in reaction prediction, indicating the potential of SynBridge in advancing chemical reaction modeling.</li></ul>

SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction

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